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- W4387389980 abstract "Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodynamic constraints simultaneously. To solve this problem, we propose a novel local planning method. The method applies a long-term dynamic window approach to generate an initial trajectory and then optimizes it with graph optimization. The method can plan actions under the robot's kinodynamic constraints in real time while allowing the generated actions to be safer and more jitterless. Experimental results show that the proposed method adapts well to crowd and static environments and outperforms most SOTA approaches." @default.
- W4387389980 created "2023-10-06" @default.
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- W4387389980 date "2023-10-04" @default.
- W4387389980 modified "2023-10-07" @default.
- W4387389980 title "Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments" @default.
- W4387389980 doi "https://doi.org/10.48550/arxiv.2310.02648" @default.
- W4387389980 hasPublicationYear "2023" @default.
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